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[EPLB][refactor] Modification of the initialization logic for expert_map and log2phy（depend on pr5285） (#5311)

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### What this PR does / why we need it?
Unify the loading logic for expert_map and log2phy.
1. The map generated when enabling the redundancy expert is incorrect.
The community generation map function only accepts the number of global
experts. When we pass in the number of logical experts plus redundant
experts, the local expert ID of the last card will index to an expert ID
that does not exist. Now we ensure that the index points to a real
existing expert ID, and each expert can be accessed. Moreover, when
redundant experts are not enabled, the output of our function remains
consistent with the community's function.
2. The map we generate is based on the length of the physical expert,
but in reality, we only need to use the length of the logical expert.
Later on, we will need to pad it accordingly, so we can simply generate
a map with the length of the logical [expert.]
3. Unify the initialization logic across different scenarios and
simplify the code for fused_moe.

**Before refactoring**

-   map path is not None：

expert map: get_rank_placement_map from _'expert_load_balancer.py'_,
maintains the map for all ranks and all layers.

log2phy: get_rank_log2phy_map from _'expert_load_balancer.py'_,
maintains the map for all ranks and all layers.

-   map path is None:

expert map: determine_expert_map from '_vllm.laye_r', The function does
not support the redundant experts of vllm-ascend.
log2phy: determine_default_log2phy_map from _'eplb_utils.py'_. The
function does not support the redundant experts of vllm-ascend.

**Refactoring**
eplb_utils.py
    init_eplb_config
         generate placement
         generate expert map
         generate log2phy

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?

Expert Mapping Test Generation:
ep size: 16, num of experts: 256, num of redundant experts: 16
+++++++++++++++++++++++++++++++++++++++++
Expert Mapping (Non-1 indicates the expert responsible for this rank)
for Rank 15:
vllm map：
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1  0  1  2  3  4  5  6  7  8
  9 10 11 12 13 14 15 16]
+++++++++++++++++++++++++++++++++++++++++
Improved map：
[16 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]

Expert Mapping Test Generation:
ep size: 16, num of experts: 256, num of redundant experts: 0
+++++++++++++++++++++++++++++++++++++++++
Expert Mapping (Non-1 indicates the expert responsible for this rank)
for Rank 15:
vllm map：
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]
+++++++++++++++++++++++++++++++++++++++
Improved map：
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]

dsr1 baselie：

| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| gsm8k-lite | 7cd45e | accuracy | gen | 100.00 |

dsr1 eplb：

| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| gsm8k-lite | 7cd45e | accuracy | gen | 100.00 |


- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef

Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
This commit is contained in:
LI SHENGYONG
2025-12-29 09:26:14 +08:00
committed by GitHub
parent 23169021d9
commit f81cf694b2
10 changed files with 168 additions and 417 deletions
Download Patch File Download Diff File Expand all files Collapse all files

0
tests/ut/ops/expert_map.json → tests/ut/eplb/core/expert_map.json
View File

86
tests/ut/eplb/core/test_eplb_utils.py
View File

@@ -1,49 +1,67 @@
import random
import os
import sys
import unittest
from unittest.mock import patch
# isort: off
import pytest
import torch
from vllm.config import VllmConfig
from vllm.model_executor.layers.fused_moe.config import (FusedMoEConfig,
FusedMoEParallelConfig
)
from vllm_ascend.eplb.core import eplb_utils
from vllm_ascend.eplb.core.eplb_utils import EPLBParamUtils
from vllm_ascend.ascend_config import init_ascend_config
from vllm_ascend.eplb.core.eplb_utils import EPLBParamUtils, init_eplb_config
# isort: on
def test_generate_log2phy_map_single_rank_holding():
class TestAscendConfig(unittest.TestCase):
expert_map = torch.tensor([[0, -1], [-1, 0]], dtype=torch.int32)
log2phy_map = eplb_utils.generate_log2phy_map(expert_map)
def setUp(self):
vllm_config = VllmConfig()
ascend_config = init_ascend_config(vllm_config)
ascend_config.dynamic_eplb = True
ascend_config.init_redundancy_expert = 2
moe_parallel_config = FusedMoEParallelConfig(2, 0, 1, 2, 1, 1, 1, 1,
True, "hccl")
moe_config = FusedMoEConfig(8, 8, 8192, 5, moe_parallel_config,
torch.float16)
moe_config.supports_eplb = True
self.ascend_config = ascend_config
self.moe_config = moe_config
self.mock_npu = patch("torch.Tensor.npu",
new=lambda self: self).start()
assert torch.all(log2phy_map[:, 0] == log2phy_map[0, 0])
assert torch.all(log2phy_map[:, 1] == log2phy_map[1, 1])
def test_init_eplb_config_with_eplb(self):
expert_map, log2phy, redundant_experts = init_eplb_config(
self.ascend_config, 0, self.moe_config)
gt_expert_map = torch.tensor([4, -1, -1, -1, 0, 1, 2, 3])
gt_log2phy = torch.tensor([9, 1, 2, 3, 5, 6, 7, 8])
self.assertTrue(torch.equal(expert_map, gt_expert_map))
self.assertTrue(torch.equal(log2phy, gt_log2phy))
self.assertEqual(redundant_experts, 2)
def test_init_eplb_config_with_eplb_withmap(self):
_TEST_DIR = os.path.dirname(__file__)
self.ascend_config.expert_map_path = _TEST_DIR + "/expert_map.json"
expert_map, log2phy, redundant_experts = init_eplb_config(
self.ascend_config, 0, self.moe_config)
gt_expert_map = torch.tensor([-1, 1, 4, -1, 2, -1, 0, 3])
gt_log2phy = torch.tensor([2, 6, 9, 3, 7, 4, 5, 8])
self.assertTrue(torch.equal(expert_map, gt_expert_map))
self.assertTrue(torch.equal(log2phy, gt_log2phy))
self.assertEqual(redundant_experts, 2)
def test_generate_log2phy_map_multiple_rank_holding(monkeypatch):
expert_map = torch.tensor([[0], [0]], dtype=torch.int32)
monkeypatch.setattr(random, "choice", lambda x: x[0])
log2phy_map = eplb_utils.generate_log2phy_map(expert_map)
assert log2phy_map.shape == (2, 1)
assert (log2phy_map >= 0).all()
def test_determine_default_log2phy_map_world_size_1():
log2phy = eplb_utils.determine_default_log2phy_map(global_expert_num=3,
world_size=1,
rank_id=0)
assert log2phy.shape == (3, )
assert (log2phy >= 0).all()
def test_determine_default_log2phy_map_world_size_multiple():
log2phy = eplb_utils.determine_default_log2phy_map(global_expert_num=6,
world_size=2,
rank_id=1)
assert log2phy.shape == (6, )
assert (log2phy >= 0).all()
def test_init_eplb_config_without_eplb(self):
self.ascend_config.dynamic_eplb = False
self.ascend_config.expert_map_path = None
expert_map, log2phy, redundant_experts = init_eplb_config(
self.ascend_config, 0, self.moe_config)
gt_expert_map = torch.tensor([-1, -1, -1, -1, 0, 1, 2, 3])
print(expert_map, log2phy, redundant_experts)
self.assertTrue(torch.equal(expert_map, gt_expert_map))
self.assertEqual(redundant_experts, 0)
class TestEPLBParamUtils:

140
tests/ut/ops/test_expert_load_balancer.py
View File

@@ -1,140 +0,0 @@
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
#
import json
import os
from typing import List, TypedDict
from unittest import mock
import torch
from tests.ut.base import TestBase
from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
class Device(TypedDict):
device_id: int
device_expert: List[int]
class Layer(TypedDict):
layer_id: int
device_count: int
device_list: List[Device]
class MockData(TypedDict):
moe_layer_count: int
layer_list: List[Layer]
class TestExpertLoadBalancer(TestBase):
def setUp(self):
_TEST_DIR = os.path.dirname(__file__)
json_file = _TEST_DIR + "/expert_map.json"
with open(json_file, 'r') as f:
self.expert_map: MockData = json.load(f)
self.expert_load_balancer = ExpertLoadBalancer(json_file, 8)
def test_init(self):
self.assertIsInstance(self.expert_load_balancer.expert_map_tensor,
torch.Tensor)
self.assertEqual(self.expert_load_balancer.layers_num,
self.expert_map["moe_layer_count"])
self.assertEqual(self.expert_load_balancer.ranks_num,
self.expert_map["layer_list"][0]["device_count"])
def test_generate_index_dicts(self):
tensor_2d = torch.tensor([[7, 2, 0, 3, 5], [6, 1, 4, 7, 2]])
result = self.expert_load_balancer.generate_index_dicts(tensor_2d)
expected_result = [{
7: 0,
2: 1,
0: 2,
3: 3,
5: 4
}, {
6: 5,
1: 6,
4: 7,
7: 8,
2: 9
}]
self.assertEqual(result, expected_result)
def test_generate_expert_placement_map(self):
expert_placement_map = self.expert_load_balancer.generate_expert_placement_map(
)
self.assertEqual(expert_placement_map.shape,
(self.expert_load_balancer.layers_num,
self.expert_load_balancer.ranks_num, 10))
self.assertTrue(torch.all(expert_placement_map >= -1))
def test_generate_log2phy_expert_map(self):
layer_id = 0
log2phy_map = self.expert_load_balancer.generate_log2phy_expert_map(
layer_id)
self.assertEqual(log2phy_map.shape,
(self.expert_load_balancer.ranks_num, 10))
self.assertTrue(torch.all(log2phy_map >= -1))
@mock.patch("torch_npu.npu._lazy_init")
@mock.patch("torch.npu.current_device", return_value="cpu")
def test_get_rank_placement_map(self, mock_current_device, mock_lazy_init):
layer_id = 0
rank_id = 0
rank_local_expert_num, rank_expert_map = self.expert_load_balancer.get_rank_placement_map(
layer_id, rank_id)
self.assertEqual(rank_local_expert_num, 5)
expected_tensor = torch.tensor([2, -1, 1, 3, -1, 4, -1, 0, -1, -1],
dtype=torch.int32).to(
rank_expert_map.device)
self.assertTrue(rank_expert_map.equal(expected_tensor))
rank_id = 1
rank_local_expert_num, rank_expert_map = self.expert_load_balancer.get_rank_placement_map(
layer_id, rank_id)
expected_tensor = torch.tensor([-1, 1, 4, -1, 2, -1, 0, 3, -1, -1],
dtype=torch.int32).to(
rank_expert_map.device)
self.assertTrue(rank_expert_map.equal(expected_tensor))
def test_get_rank_log2phy_map(self):
layer_id = 0
rank_id = 0
log2phy_map = self.expert_load_balancer.get_rank_log2phy_map(
layer_id, rank_id)
expected_tensor = torch.tensor([2, 6, 1, 3, 7, 4, 5, 0, -1, -1],
dtype=torch.int32).to(
log2phy_map.device)
self.assertTrue(log2phy_map.equal(expected_tensor))
rank_id = 1
log2phy_map = self.expert_load_balancer.get_rank_log2phy_map(
layer_id, rank_id)
expected_tensor = torch.tensor([2, 6, 9, 3, 7, 4, 5, 8, -1, -1],
dtype=torch.int32).to(
log2phy_map.device)
self.assertTrue(log2phy_map.equal(expected_tensor))
def test_get_global_redundant_expert_num(self):
redundant_expert_num = self.expert_load_balancer.get_global_redundant_expert_num(
)
expected_redundant_expert_num = len(self.expert_map["layer_list"][0]["device_list"][0]["device_expert"]) * \
self.expert_map["layer_list"][0]["device_count"] - 8
self.assertEqual(redundant_expert_num, expected_redundant_expert_num)

4
tests/ut/ops/test_fused_moe.py
View File

@@ -117,8 +117,8 @@ def mock_dist_env(mocker: MockerFixture):
enable_multistream_moe=False,
expert_map_path=None
)), \
patch('vllm_ascend.ops.fused_moe.fused_moe.determine_expert_map',
return_value=(3, torch.tensor([0, 1, 2, -1, -1, -1, -1, -1]))), \
patch('vllm_ascend.ops.fused_moe.fused_moe.init_eplb_config',
return_value=(torch.tensor([0, 1, 2, -1, -1, -1, -1, -1]), None, 0)), \
patch('vllm_ascend.ops.fused_moe.fused_moe.get_forward_context',
return_value=mock_forward_context_obj), \
patch('vllm_ascend.ops.fused_moe.prepare_finalize.get_forward_context',

154
vllm_ascend/eplb/core/eplb_utils.py
View File

@@ -15,87 +15,111 @@
# This file is a part of the vllm-ascend project.
#
# Todo: Once https://github.com/vllm-project/vllm/issues/22246 is merged in vllm. Remove eplb utils.
import json
import os.path
import random
import sys
from collections import defaultdict
import numpy as np
import torch
from vllm.logger import logger
import vllm_ascend.envs as envs_ascend
def generate_log2phy_map(expert_map):
num_local_experts = expert_map.max() + 1
log2phy_map = expert_map.clone()
num_ranks, num_global_expert = log2phy_map.shape
def expert_file_to_tensor(expert_map_path, layer_id):
with open(expert_map_path, "r") as f:
data = json.load(f)
physical_count = 0
device_data = []
if layer_id > data["moe_layer_count"]:
raise ValueError("Invalid EPLB Table")
if layer_id == data["moe_layer_count"]:
logger.warning("Init expert map of mtp/eagle when using sample.")
return None, None
for device in data["layer_list"][layer_id]["device_list"]:
physical_count += len(device["device_expert"])
device_data.append(device["device_expert"])
global_placement = torch.tensor(device_data, dtype=torch.int32)
return global_placement, physical_count
row_indices = torch.arange(num_ranks).view(-1, 1).expand(num_ranks, \
num_global_expert) * num_local_experts
log2phy_map[log2phy_map != -1] += row_indices[log2phy_map != -1]
for idx in range(num_global_expert):
positive_rank_idx = torch.where(log2phy_map[:, idx] != -1)[0]
negative_rank_idx = torch.where(log2phy_map[:, idx] == -1)[0]
num_rank_holding_expert = positive_rank_idx.size(0)
if num_rank_holding_expert == 0:
log2phy_map[:, idx] = torch.full((num_ranks, ),
0,
dtype=log2phy_map.dtype)
if num_rank_holding_expert == 1:
log2phy_map[negative_rank_idx, idx] = torch.full(
(num_ranks - 1, ),
log2phy_map[positive_rank_idx, idx].item(),
dtype=log2phy_map.dtype)
def generate_global_placement(n_expert, ep_size, n_redundant):
all_experts = np.arange(n_expert)
groups = np.array_split(all_experts, ep_size)
for i in range(n_redundant):
j = i % ep_size + 1
if len(groups[-j]) == 0:
groups[-j] = np.append(groups[-j], j)
else:
try:
random_list = [
random.choice(log2phy_map[positive_rank_idx, idx])
for _ in range(num_ranks - num_rank_holding_expert)
]
log2phy_map[negative_rank_idx,
idx] = torch.tensor(random_list,
dtype=log2phy_map.dtype)
except Exception as e:
logger.error(f"Fail to get log2phy_map: {str(e)}")
groups[-j] = np.append(groups[-j], (groups[-j][-1] + 1) % n_expert)
return torch.tensor(groups, dtype=torch.int32)
def init_eplb_config(ascend_config, layer_id, moe_config):
expert_map_path = ascend_config.expert_map_path
n_experts = moe_config.num_experts
ep_size = moe_config.ep_size
global_placement = None
eplb_enable = ascend_config.dynamic_eplb or ascend_config.expert_map_record_path
n_redundant = ascend_config.init_redundancy_expert if eplb_enable else 0
if expert_map_path:
if not (os.path.exists(expert_map_path)
and os.access(expert_map_path, os.R_OK)):
raise ValueError("Invalid EPLB path")
eplb_enable = True
global_placement, physical_count = expert_file_to_tensor(
expert_map_path, layer_id)
if physical_count is not None:
n_redundant = physical_count - n_experts
if not moe_config.supports_eplb:
raise ValueError(
"Eplb supports only w8a8_dynamic quantization.")
else:
eplb_enable = False
if global_placement is None:
global_placement = generate_global_placement(n_experts, ep_size,
n_redundant)
if ep_size == 1:
return None, None, n_redundant
global_expert_map = []
for rankid in range(ep_size):
expert_map = torch.full((n_experts, ), -1, dtype=torch.int32)
local_placement = global_placement[rankid]
expert_map[local_placement] = torch.arange(local_placement.shape[0],
dtype=torch.int32)
global_expert_map.append(expert_map)
local_expert_map = global_expert_map[moe_config.ep_rank].npu()
log2phy = generate_log2phy_map(
global_expert_map, moe_config.ep_rank).npu() if eplb_enable else None
return local_expert_map, log2phy, n_redundant
def generate_log2phy_map(global_expert_map, ep_rank):
log2phy_map = defaultdict(list)
valid_count = torch.sum(global_expert_map[0] != -1)
for rankid, map_per_rank in enumerate(global_expert_map):
for idx, val in enumerate(map_per_rank):
val = val.item()
# 计算value：当前值 + i * 有效元素个数
if val != -1:
log2phy_map[idx].append(val + rankid * valid_count)
for key in log2phy_map.keys():
num_of_duplications = len(log2phy_map[key])
log2phy_map[key] = log2phy_map[key][ep_rank % num_of_duplications]
log2phy_map = torch.scatter(
torch.zeros(len(log2phy_map.keys()), dtype=torch.int32), 0,
torch.tensor(list(log2phy_map.keys()), dtype=torch.int64),
torch.tensor(list(log2phy_map.values()), dtype=torch.int32))
return log2phy_map
def determine_default_log2phy_map(global_expert_num, world_size, rank_id):
if world_size == 1:
local_ids = torch.arange(global_expert_num, dtype=torch.int32)
expert_map_all = local_ids.unsqueeze(0).expand(world_size, -1)
log2phy_map_all = generate_log2phy_map(expert_map_all)
return log2phy_map_all[rank_id]
local_num_experts = global_expert_num // world_size
expert_map_all = torch.full((world_size, global_expert_num),
-1,
dtype=torch.int32)
for r in range(world_size):
if r < world_size - 1:
start = r * local_num_experts
end = (r + 1) * local_num_experts
local_count = local_num_experts
else:
start = r * local_num_experts
end = global_expert_num
local_count = global_expert_num - r * local_num_experts
if isinstance(local_count, int):
local_ids = torch.arange(local_count, dtype=torch.int32)
expert_map_all[r, start:end] = local_ids
log2phy_map_all = generate_log2phy_map(expert_map_all)
return log2phy_map_all[rank_id]
class EPLBParamUtils:
@staticmethod

4
vllm_ascend/eplb/core/eplb_worker.py
View File

@@ -377,8 +377,8 @@ class EplbWorker:
maps.append(new_expert_map[self.rank_id].numpy().tolist())
log2phy_map = generate_log2phy_map(new_expert_map)
log2phy_all.append(log2phy_map[self.rank_id].numpy().tolist())
log2phy_map = generate_log2phy_map(new_expert_map, self.rank_id)
log2phy_all.append(log2phy_map.numpy().tolist())
layer_ids.append(layer_id)

118
vllm_ascend/ops/expert_load_balancer.py
View File

@@ -1,118 +0,0 @@
import json
import random
from typing import Dict, List
import torch
import torch.distributed as dist
class ExpertLoadBalancer(object):
def __init__(self, expert_map_path, num_experts):
self.expert_map_path = expert_map_path
self.num_experts = num_experts
self.tensor_data = []
self.expert_map_tensor, self.layers_num, self.ranks_num = (
self._expert_file_to_tensor())
self.global_expert_num = num_experts + self.get_global_redundant_expert_num(
)
self.expert_placement_map = self.generate_expert_placement_map()
def _expert_file_to_tensor(self):
with open(self.expert_map_path, "r") as f:
data = json.load(f)
layers_num = data["moe_layer_count"]
gpus_num = data["layer_list"][0]["device_count"]
for layer in data["layer_list"]:
device_data = []
for device in layer["device_list"]:
device_data.append(device["device_expert"])
self.tensor_data.append(device_data)
expert_map_tensor = torch.tensor(self.tensor_data, dtype=torch.int32)
return expert_map_tensor, layers_num, gpus_num
def generate_index_dicts(self, tensor_2d):
dict_list = []
current_idx = 0
for row in tensor_2d:
value_to_index = {}
for i in range(row.size(0)):
value = row[i].item()
value_to_index[value] = current_idx + i
dict_list.append(value_to_index)
current_idx += row.size(0)
return dict_list
def generate_expert_placement_map(self):
expert_placement_map = torch.full(
(self.layers_num, self.ranks_num, self.global_expert_num),
-1,
dtype=torch.int32,
)
for layer_id in range(self.layers_num):
for gpu_id in range(self.ranks_num):
e_ids = self.expert_map_tensor[layer_id, gpu_id]
expert_placement_map[layer_id, gpu_id,
e_ids] = torch.arange(len(e_ids),
dtype=torch.int32)
return expert_placement_map
def generate_log2phy_expert_map(self, layer_id):
concatenated = torch.flatten(self.expert_map_tensor[layer_id])
rank_expert_to_global = self.generate_index_dicts(
self.expert_map_tensor[layer_id])
result_dict: Dict[int, List[int]] = {}
for idx, value in enumerate(concatenated):
key = value.item()
if key not in result_dict:
result_dict[key] = []
result_dict[key].append(idx)
log2phy_map = torch.full((self.ranks_num, self.global_expert_num),
-1,
dtype=torch.int32)
for rank in range(self.ranks_num):
for key in result_dict:
indices_in_concat = result_dict[key]
if key in rank_expert_to_global[rank]:
log2phy_map[rank][key] = rank_expert_to_global[rank][key]
else:
chosen_index = random.choice(indices_in_concat)
log2phy_map[rank][key] = chosen_index
return log2phy_map
def get_rank_placement_map(self, layer_id, rank_id):
layer_expert_map = self.expert_placement_map[layer_id]
rank_expert_map = layer_expert_map[rank_id].to(
torch.npu.current_device())
rank_local_expert_num = torch.sum(torch.ne(rank_expert_map, -1)).item()
return rank_local_expert_num, rank_expert_map
def get_rank_log2phy_map(self, layer_id, rank_id):
layer_log2phy_map = self.generate_log2phy_expert_map(layer_id)
return layer_log2phy_map[rank_id]
def get_global_redundant_expert_num(self):
global_redundant_expert_num = (
len(self.expert_map_tensor[0][0]) * self.ranks_num -
self.num_experts)
return global_redundant_expert_num
def check_expert_map_tensor(self):
if dist.is_initialized():
try:
rank = dist.get_rank()
world_size = dist.get_world_size()
all_expert_maps = [None for _ in range(world_size)]
dist.all_gather_object(all_expert_maps, self.tensor_data)
for rank_id, expert_map_tensor in enumerate(all_expert_maps):
if self.tensor_data != expert_map_tensor:
raise ValueError(
f"The expert map of rank{rank} is not equal to rank{rank_id}"
)
return True
except Exception as e:
raise ValueError(
f"The expert maps of all ranks are inconsistency: {e}")

73
vllm_ascend/ops/fused_moe/fused_moe.py
View File

@@ -14,7 +14,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os.path
from typing import Any, Callable, Optional
import torch
@@ -25,19 +24,17 @@ from vllm.forward_context import get_forward_context
from vllm.logger import logger
from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
from vllm.model_executor.layers.fused_moe.layer import (
FusedMoE, UnquantizedFusedMoEMethod, determine_expert_map,
get_compressed_expert_map)
FusedMoE, UnquantizedFusedMoEMethod, get_compressed_expert_map)
from vllm.model_executor.layers.fused_moe.shared_fused_moe import \
SharedFusedMoE
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.ascend_forward_context import MoECommType
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.eplb.core.eplb_utils import determine_default_log2phy_map
from vllm_ascend.eplb.core.eplb_utils import init_eplb_config
from vllm_ascend.eplb.utils import moe_load_async_stream
from vllm_ascend.flash_common3_context import (get_flash_common3_context,
set_flash_common3_context)
from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
from vllm_ascend.ops.fused_moe.experts_selector import select_experts
from vllm_ascend.ops.fused_moe.moe_comm_method import (AllGatherCommImpl,
setup_moe_comm_method)
@@ -164,11 +161,8 @@ class AscendFusedMoE(FusedMoE):
self.moe_config.dp_group = get_dp_group()
self.moe_config.ep_group = get_ep_group()
self.moe_config.mc2_group = get_mc2_group()
self.moe_config.supports_eplb = self.quant_method.supports_eplb
ascend_config = get_ascend_config()
self.dynamic_eplb = ascend_config.dynamic_eplb or ascend_config.expert_map_record_path
self.expert_map_path = ascend_config.expert_map_path
self.global_redundant_expert_num = ascend_config.init_redundancy_expert
self.global_num_experts = num_experts + self.global_redundant_expert_num
# flashcommon3 gate stream
self.multistream_overlap_gate = ascend_config.multistream_overlap_gate
if self.multistream_overlap_gate and AscendFusedMoE.gate_stream is None:
@@ -178,66 +172,33 @@ class AscendFusedMoE(FusedMoE):
self.e_score_correction_bias.data = self.e_score_correction_bias.data.to(
dtype=vllm_config.model_config.dtype)
# init moe.
self.local_num_experts, self._expert_map, _ = determine_expert_map(
self.ep_size, self.ep_rank, self.global_num_experts)
# TODO: Temporary flag to indicate if static EPLB is enabled. This is a
# workaround to bypass a quantization check that fails with float weights.
init_eplb_enable = False
# static eplb initializing with expert_map_path
if self.expert_map_path and os.path.exists(
self.expert_map_path) and os.access(self.expert_map_path,
os.R_OK):
self.expert_load_balancer = ExpertLoadBalancer(
self.expert_map_path, num_experts)
self.expert_load_balancer.check_expert_map_tensor()
self.global_redundant_expert_num = (
self.expert_load_balancer.get_global_redundant_expert_num())
self.global_num_experts = num_experts + self.global_redundant_expert_num
try:
self.local_num_experts, self._expert_map = (
self.expert_load_balancer.get_rank_placement_map(
self.moe_instance_id, self.ep_rank))
self.log2phy = self.expert_load_balancer.get_rank_log2phy_map(
self.moe_instance_id, self.ep_rank).npu()
init_eplb_enable = True
except Exception as e:
logger.warning(
f"Init expert map of mtp/eagle when using sample.{e}")
self.log2phy = determine_default_log2phy_map(
self.global_num_experts, self.ep_size, self.ep_rank).npu()
else:
# dynamic eplb initializing with not expert_map_path
if self.dynamic_eplb:
self.log2phy = determine_default_log2phy_map(
self.global_num_experts, self.ep_size, self.ep_rank).npu()
if self._expert_map is not None and isinstance(self._expert_map,
torch.Tensor):
# init moe
self._expert_map, self.log2phy, self.global_redundant_expert_num = init_eplb_config(
ascend_config, self.moe_instance_id, self.moe_config)
self.global_num_experts = num_experts + self.global_redundant_expert_num
self.dynamic_eplb = (ascend_config.dynamic_eplb
or ascend_config.expert_map_record_path) and (
self.log2phy is not None)
self.local_num_experts = (torch.sum(
self._expert_map != -1) if self._expert_map is not None else
self.global_num_experts)
if self._expert_map is not None:
logger.info_once(
"[EP Rank %s/%s] Expert parallelism is enabled. Local/global"
" number of experts: %s/%s. Experts local to global index map:"
" %s.", self.ep_rank, self.ep_size, self.local_num_experts,
self.global_num_experts,
get_compressed_expert_map(self._expert_map))
local_num_experts = (torch.sum(
self._expert_map != -1) if self._expert_map is not None else
self.global_num_experts)
if self.dynamic_eplb:
self.moe_load = torch.zeros(local_num_experts,
self.moe_load = torch.zeros(self.local_num_experts,
dtype=torch.int64).npu()
if init_eplb_enable and (
not hasattr(self.quant_method, "quant_method")
or not isinstance(self.quant_method.quant_method,
AscendW8A8DynamicFusedMoEMethod)):
raise ValueError("Eplb supports only w8a8_dynamic quantization.")
self.moe_config.num_experts = self.global_num_experts
self.moe_config.num_local_experts = self.local_num_experts
self.moe_config.original_num_experts = num_experts
moe_quant_params = {
"num_experts": local_num_experts,
"num_experts": self.local_num_experts,
"hidden_size": self.hidden_size,
"intermediate_size_per_partition":
self.intermediate_size_per_partition,
@@ -373,7 +334,7 @@ class AscendFusedMoE(FusedMoE):
renormalize=self.renormalize,
use_grouped_topk=self.use_grouped_topk,
global_num_experts=self.global_num_experts,
expert_map=self.expert_map,
expert_map=self._expert_map,
topk_group=self.topk_group,
num_expert_group=self.num_expert_group,
custom_routing_function=self.custom_routing_function,

5
vllm_ascend/quantization/quant_config.py
View File

@@ -536,6 +536,11 @@ class AscendFusedMoEMethod(FusedMoEMethodBase):
# TODO: implement this function
pass
@property
def supports_eplb(self):
supports_eplb = getattr(self.quant_method, "supports_eplb", False)
return supports_eplb
class AscendEmbeddingMethod(AscendLinearMethod):
"""Embedding method for Ascend quantization.

1
vllm_ascend/quantization/w8a8_dynamic.py
View File

@@ -115,6 +115,7 @@ class AscendW8A8DynamicFusedMoEMethod:
self.dynamic_eplb = ascend_config.dynamic_eplb or ascend_config.expert_map_record_path
self.in_dtype = vllm_config.model_config.dtype
self.supports_eplb = True
try:
device_group = get_mc2_group().device_group